将带有嵌套循环的函数转换为lambda表达式（python）

Question

我正在尝试将函数转换为lambda表达式，以最大程度地减少计算时间。更具体地说，我想相对于plot2的所有点（其中plot1，plot2数据帧带有经度，纬度列）保持对plot1的每个点的最小正弦距离。这是我的代码：

def calculate_min_haversine_distance(plot1, plot2):

    for index,row in plot1.iterrows():
        minimum = 100000000
        for index2, row2 in plot2.iterrows():
            dis = haversine_distance(row.latitude, row.longitude, row2.latitude, row2.longitude) 
            if (dis<minimum):
                minimum=dis
        plot1.loc(index,'Min Haversine Distance') = minimum

    return plot1

Answer 1

我不确定如何摆脱第一个循环，但这应该可以帮助您摆脱第二个循环：

def calculate_min_haversine_distance(plot1, plot2):
    for index,row in plot1.iterrows():
        plot2['dist'] = plot2.apply(lambda x: haversine_distance(row.latitude, row.longitude, x.latitude, x.longitude), axis=1)
        plot1.loc[index,'Min Haversine Distance'] = min(plot2['dist'])
    plot2.drop('dist', axis=1, inplace=True) # Delete the temporary column created
    return plot1

Answer 2

我将尝试执行以下操作：希望对您有所帮助。

import pandas as pd
import numpy as np


df1 = pd.DataFrame(data={'lat': [1,2,3,4], 'lon': [5,6,7,8]})
df2 = pd.DataFrame(data={'lat': [9,10,11,12], 'lon': [13,14,15,16]})
df1['key'], df2['key'] = 1,1

df_c = pd.merge(df1, df2, on='key').drop('key', axis=1)

# below function is copied from: https://stackoverflow.com/a/43577275/4450090
def haversine(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371):
    if to_radians:
        lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])

    a = np.sin((lat2-lat1)/2.0)**2 + \
        np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2

    return earth_radius * 2 * np.arcsin(np.sqrt(a))

df_c['dist'] = df_c.apply(lambda x: haversine(x['lat_x'], x['lon_x'], x['lat_y'], x['lon_y']), axis=1)
min_val = 1000000
df_c['dist'] = df_c['dist'].apply(lambda x: x if x < min_val else min_val)